1. Technical Field
The present invention is related to a method for determining a parameter set which is designed to be used for determining the pose of a camera with regard to at least one real object captured by the camera and/or for determining a three-dimensional structure of the at least one real object.
2. Background Information
Many applications in the field of computer vision require aligning two images with subpixel precision, such as described in Irani, M. & Anandan, P. Vision Algorithms '99, All about direct methods, Springer-Verlag Berlin Heidelberg, 2000, 267-277 ([1]), for example image mosaicing and super resolution, it naturally may also serve as basis for augmented reality applications. While there exists a whole body of literature on planar template tracking as well as sequential structure from motion, there is known to be only one method, such as described in Silveira, G. & Malis, E. Unified direct visual tracking of rigid and deformable surfaces under generic illumination changes in grayscale and color images IJCV, 2010, 89, 84-105 ([2]), that also recovers the shape of the object depicted in the reference image simultaneously to the estimation of the relative camera motion. This will be explained in more detail below.
In the field of monocular deformable template tracking, a variety of methods exist. There are direct and indirect methods, the former class working exclusively on intensity values while the latter are also using abstractions such as feature points or lines. Feature points have the advantage that they can establish correspondences also in presence of relatively large baselines, while the computational cost of computing these correspondences is high given no (offline) training phase. Direct methods however rely on relatively small baselines, but are very precise due to using all available information.
J. Pilet, V. Lepetit, and P. Fua. Fast non-rigid surface detection, registration and realistic augmentation. IJCV, 76(2):109-112, 2007 ([7]) use a coarse point-based detection method to obtain the approximate registration. After that they refine by deforming a triangular mesh in image space. Their method is robust to a high amount of outliers. However, it is designed to only work on single initially planar objects such as a piece of paper, also the optimization is carried out exclusively in image space.